Information processing system and program

Abstract

To provide a system that can evaluate image quality in a manner that reflects a user's preferences, as compared with a configuration in which image quality is evaluated based on uniform criteria.SOLUTION: A diagnostic server stores weight values for respective evaluation items related to image quality in a storage device, acquires image data to be evaluated, evaluates the image data for each evaluation item based on evaluation criteria for each evaluation item, and evaluates the image quality of the image related to the image data based on evaluation results in the evaluation for each evaluation item and the weight values for the respective evaluation items stored in the storage device.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The present invention relates to an information processing system and a program. [Background technology]

[0002] The quality of an image is evaluated by setting an evaluation standard. Patent Document 1 discloses a method of learning the relationship between the characteristics of a partial image at a position on an image that a subject focuses on when determining an overall image quality score and an image quality evaluation value calculated according to the characteristics, specifying the position of the partial image to be evaluated for image quality evaluation items required for determining an overall image quality score for the image to be evaluated based on the learning result, calculating an image quality evaluation value for the image quality evaluation items required for determining an overall image quality score for the partial image information at the specified position, and calculating an overall image quality score for the image to be evaluated based on the calculated image quality evaluation value and the learning result. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 10-63859 Summary of the Invention [Problem to be solved by the invention]

[0004] In image diagnosis, image quality is evaluated based on predetermined criteria for each item. However, different users may place emphasis on different evaluation items, and it may be necessary to reflect the preferences of such users in the evaluation of image quality.

[0005] An object of the present invention is to realize a system capable of evaluating image quality in a manner that reflects the user's preferences, as compared to a configuration in which image quality is evaluated based on uniform standards. [Means for solving the problem]

[0006] The present invention according to claim 1 is one or more processors; The processor, A weight value for each evaluation item related to image quality is stored in a storage device; Acquire image data to be evaluated; The image data is evaluated for each evaluation item based on an evaluation criterion for each evaluation item; evaluating the image quality of the image related to the image data based on an evaluation result in the evaluation for each of the evaluation items and a weighting value for the evaluation item stored in the storage device; The present invention relates to an information processing system. The present invention according to claim 2 is the storage device holds a table in which the sets of weight values ​​for the plurality of evaluation items correspond to output characteristics of an image; The processor, Acquire information specifying the output characteristics of a device that outputs the image data in response to a user operation; based on the table, associating a set of weight values ​​corresponding to output characteristics of a device identified by the information; With respect to the image output by the device, applying the set of weight values ​​associated with the device to the evaluation results for each of the evaluation items to evaluate the image quality. The information processing system according to claim 1 is characterized in that: The present invention according to claim 3 is The processor, accepting individual changes to weights for one or more evaluation items in the set of weights for each device associated with the set of weights in response to a user operation, and updating the set of weights; The information processing system according to claim 2 is characterized in that: The present invention according to claim 4 is The processor, accepting a change to adjust each weight value included in the set of weight values ​​in each device associated with the set of weight values ​​within a predetermined range in response to a user operation, and updating the set of weight values; The information processing system according to claim 2 is characterized in that: The present invention according to claim 5 is The processor, acquiring history information relating to image output from a device that outputs the image data; updating the weighting value applied to the evaluation of the image output by the device based on information regarding image quality included in the acquired history information; applying the updated weighting value to the evaluation result for each evaluation item for the image output by the device to evaluate the image quality. The information processing system according to claim 1 is characterized in that: The present invention according to claim 6 is The processor, When a setting related to image quality in the device satisfies a predetermined condition in a plurality of image outputs by the device, the weight value is updated based on the history information in the plurality of image outputs. The information processing system according to claim 5 is characterized in that: The present invention according to claim 7 is On the computer, A function of storing weight values ​​for each evaluation item related to image quality in a storage device; A function for acquiring image data to be evaluated; a function of evaluating the image data for each evaluation item based on evaluation criteria for each evaluation item; a function of evaluating the image quality of the image related to the image data based on an evaluation result in the evaluation for each of the evaluation items and a weight value for the evaluation item stored in the storage device; The present invention is a program characterized by realizing the above. Effect of the Invention

[0007] According to the invention of claim 1, compared to a configuration in which image quality is evaluated based on a uniform standard, it is possible to evaluate image quality in a manner that reflects the user's preferences. According to the invention of claim 2, the effort required for setting weight values ​​can be reduced, compared to a configuration in which the user inputs the weight values ​​of each evaluation item individually. According to the invention of claim 3, compared to a configuration in which image quality is evaluated based on weighting values ​​set according to output characteristics, it is possible to perform evaluation according to individual settings for each device. According to the invention of claim 4, in comparison with a configuration in which image quality is evaluated based on weight values ​​set according to output characteristics, it is possible to perform evaluation according to settings for each device. According to the invention of claim 5, the effort required for setting weight values ​​can be reduced, compared to a configuration in which the user inputs the weight values ​​of each evaluation item depending on the use of the device. According to the invention of claim 6, unlike a configuration in which the weighting value is updated based on one image output, it is possible to reflect the user's preference regarding image quality. According to the invention of claim 7, in a computer having the program of the present invention installed, it is possible to evaluate image quality that reflects the user's preferences, as compared to a configuration that evaluates image quality based on uniform criteria. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing a configuration of a diagnostic system to which the present embodiment is applied. [Diagram 2] FIG. 1 is a diagram illustrating a configuration of an image processing device. [Diagram 3] FIG. 2 illustrates an example of the configuration of a computer that realizes a diagnostic server. [Figure 4] FIG. 13 is a diagram illustrating an example of the configuration of a computer that realizes a terminal device. [Diagram 5] FIG. 13 is a diagram showing the procedure of evaluation by the diagnostic server. [Figure 6]6A and 6B are diagrams for explaining a set of weight values ​​determined according to output characteristics, in which FIG. 6A shows an example of input information by a user, FIG. 6B shows an example of a correspondence table between input information and JOB categories, and FIG. 6C shows an example of a setting table for weight values ​​in JOB categories. [Figure 7] 7A and 7B are diagrams showing examples of weighting values ​​set for individual image processing devices, where FIG. 7(A) shows weighting values ​​set based on selected items, and FIG. 7(B) shows changes to the weighting values ​​for individual evaluation items. [Figure 8] FIG. 13 is a diagram illustrating an example of a weight value adjustment table. [Figure 9] FIG. 13 is a diagram showing a state in which the weight values ​​for each evaluation item are adjusted. [Figure 10] FIG. 13 is a diagram illustrating an example of setting weight values ​​for each image processing device. [Figure 11] FIG. 11 is a diagram showing an example of a weight value set prepared corresponding to an operation mode. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. <System configuration> FIG. 1 is a diagram showing the configuration of a diagnostic system to which the present embodiment is applied. This diagnostic system includes an image processing device 100, a diagnostic server 200, and a terminal device 300. The image processing device 100 is a device that forms and outputs an image on a recording material such as paper using an image forming material such as toner. The image processing device 100 also has a function of reading the output image and transmitting it to the diagnostic server 200. The diagnostic server 200 is a server that evaluates the image received from the image processing device 100 and diagnoses image quality. The terminal device 300 is an information processing device that acquires the diagnostic result of the diagnostic server 200 and presents it to an administrator or the like.

[0010] The image processing device 100 and the diagnostic server 200, and the diagnostic server 200 and the terminal device 300 are connected via a network. The network is not particularly limited as long as it can be used for data communication between the devices. For example, a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, etc. may be used. The communication line used for data communication may be wired or wireless, and these may be used in combination. A gateway device, a router, an access point, or other relay device may be used to connect each device via multiple networks or communication lines. The connection between the diagnostic server 200 and the terminal device 300 may be the same network as the network used to connect the image processing device 100 and the diagnostic server 200, or a different network may be used. In addition, when the diagnostic server 200 is configured as a local server machine and is located in the same place as the terminal device 300, the diagnostic server 200 and the terminal device 300 may be directly connected to each other.

[0011] <Configuration of image processing device 100> 2 is a diagram showing the configuration of the image processing device 100. The image processing device 100 includes an image forming unit 110, an image reading unit 120, a display device 130, an operation device 140, a communication interface 150, a storage device 160, and a control device 170.

[0012] The image forming unit 110 forms an image based on image data on a recording material using an image forming material. Examples of a method for forming an image on a recording material include an electrophotographic method in which toner attached to a photoreceptor is transferred to the recording material to form an image, and an inkjet method in which ink is ejected onto the recording material to form an image.

[0013] The image reading unit 120 is configured by a so-called scanner device, optically reads an image on a set document, and generates data of the read image. Examples of image reading methods include a CCD method in which light reflected from light irradiated from a light source onto a document is reduced by a lens and received by a CCD (Charge Coupled Devices), and a CIS method in which light reflected from light irradiated in sequence from an LED (Light Emitting Diode) light source onto a document is received by a CIS (Contact Image Sensor).

[0014] The display device 130 displays information images that present various information to a user of the image processing device 100, preview images of images to be processed such as read and output, operation images for the user to perform operations, etc. The display device 130 is configured, for example, with a liquid crystal display. The display device 130 may be combined with the operation device 140 to be used as a user interface means for the user to input and output information to and from the image processing device 100.

[0015] The operation device 140 is a device for a user to perform operations such as inputting commands and data. The operation device 140 is configured, for example, by a hardware key, a touch sensor that outputs a control signal according to a position pressed or touched by a finger, etc. It may be configured as a touch panel that combines a touch sensor and a liquid crystal display that constitutes the display device 130.

[0016] The communication interface 150 is an interface for transmitting and receiving commands and data to and from an external device. An interface according to a communication method with the external device is used as the communication interface 150. The connection with the external device may be made via a network or by direct connection. The communication line may be a wired line or a wireless line. Furthermore, if the image processing device 100 has a facsimile function, the communication interface 150 includes an interface for a telephone line.

[0017] The storage device 160 stores various data, such as programs and data executed by the control device 170, image data read by the image reading unit 120, log data generated by various operations, etc. The storage device 160 is realized by a storage device such as a magnetic disk device or an SSD (Solid State Drive), for example.

[0018] The control device 170 includes a processor as a calculation means and a memory as a storage means, and performs various data processing and controls the image processing device 100 by loading a program stored in the storage device 160 into the memory and executing it. As the processor, in addition to a central processing unit (CPU), a micro processing unit (MPU), a graphics processing unit (GPU), a digital signal processor (DSP), etc. are used. As the memory, for example, a dynamic random access memory (DRAM), etc. are used.

[0019] <Configuration of diagnostic server 200> FIG. 3 is a diagram showing an example of the configuration of a computer that realizes the diagnostic server 200. The diagnostic server 200 includes one or more processors 201 that are calculation means, and a main memory 202 and an auxiliary memory 203 that are storage means. The processor 201 realizes each function of the diagnostic server 200 by loading a program stored in the auxiliary memory 203 into the main memory 202 and executing it. For example, a central processing unit (CPU), a micro processing unit (MPU), a graphics processing unit (GPU), or a digital signal processor (DSP) is used as the processor 201. For example, a random access memory (RAM) is used as the main memory 202. For example, a magnetic disk device or a solid state drive (SSD) is used as the auxiliary memory 203. The diagnostic server 200 also includes a communication interface 204 for connecting to the image processing device 100 and the terminal device 300 via a network.

[0020] The diagnostic server 200 evaluates and diagnoses the image acquired from the image processing device 100 by the processor 201 executing a program. The diagnostic server 200 is an example of an information processing system. The details of the evaluation and diagnosis of the image by the diagnostic server 200 will be described later. Note that the configuration of the computer shown in FIG. 3 is only an example, and the computer realizing the diagnostic server 200 is not limited to the configuration shown in FIG. 3. For example, the configuration may include a non-volatile memory such as a flash memory or a ROM (Read Only Memory) as a storage device. Furthermore, the diagnostic server 200 is not limited to a single computer as shown in FIG. 3, but may be configured by distributing functions among multiple computers, or may be realized as a so-called cloud server constructed using resources on a network.

[0021] <Configuration of Terminal Device 300> 4 is a diagram showing an example of the configuration of a computer that realizes the terminal device 300. The terminal device 300 includes one or more processors 301, which are calculation means, and a main memory 302 and an auxiliary memory 303, which are storage means. The processor 301 realizes each function of the terminal device 300 by loading a program stored in the auxiliary memory 303 into the main memory 302 and executing it. For example, a CPU, an MPU, a GPU, a DSP, or the like is used as the processor 301. For example, a RAM is used as the main memory 302. For example, a magnetic disk device, an SSD, or the like is used as the auxiliary memory 303.

[0022] The terminal device 300 also includes a display device 304 that displays various screens, and an input device 305 on which a user performs input operations. The display device 304 is, for example, a liquid crystal display. The input device 305 is, for example, a touch sensor, a keyboard, or the like. The input device 305 may be configured as a touch panel that combines the touch sensor of the input device 305 and the liquid crystal display of the display device 304. The terminal device 300 also includes a communication interface 306 for connecting to the diagnostic server 200 via a network. For example, a smartphone, a tablet terminal, a notebook personal computer, or the like may be used as the terminal device 300. Note that the configuration of the computer shown in FIG. 4 is merely an example, and the computer used as the terminal device 300 is not limited to the configuration example of FIG. 4. For example, the configuration may include a non-volatile memory such as a flash memory or a ROM as a storage device.

[0023] <Image Evaluation by Diagnostic Server 200> Next, the evaluation and diagnosis of an image by the diagnostic server 200 will be described. As a preliminary operation, an image formed on a recording material by the image forming section 110 of the image processing device 100 is output, and the output image is read by the image reading section 120 of the image processing device 100 to obtain read data (image data) of the output image. Then, this image data is transmitted from the image processing device 100 to the diagnostic server 200, and becomes the subject of evaluation and diagnosis by the diagnostic server 200.

[0024] FIG. 5 is a diagram showing the procedure of evaluation by the diagnostic server 200. The diagnostic server 200 first evaluates an image for evaluation items related to image quality divided into a plurality of categories. Then, evaluation is performed for each category based on the evaluation results for each evaluation item, and the image quality of the entire image is evaluated based on the evaluation results for each category. Evaluation for each evaluation item is performed using a score (hereinafter referred to as "evaluation score") specified according to an evaluation criterion previously set for each evaluation item. In the example shown in FIG. 5, "density", "intra-plane unevenness", "periodic unevenness", "sudden streaks", "periodic streaks", "stain", "whiteout", "roughness", etc. are set as evaluation items, and "density", "intra-plane unevenness", "defect", "noise", etc. are set as categories. The evaluation item "density" is included in the category "density". The evaluation items "intra-plane unevenness" and "periodic unevenness" are included in the category "intra-plane unevenness". The evaluation items "sudden streaks", "periodic streaks", "stain" and "whiteout" are included in the category "defect". The evaluation item "roughness" is included in the category "noise."

[0025] As described above, the diagnostic server 200 first specifies an evaluation score for each evaluation item for the image to be diagnosed based on the evaluation criteria for each evaluation item. There are no particular limitations on the evaluation items and evaluation criteria. The specific evaluation items and evaluation criteria may be set by the provider of the diagnostic system or by the user of the diagnostic system. In addition, the initial settings by the provider of the diagnostic system may be changeable by the user. Similarly, the classification of categories may be configured to be set by the provider or user of the diagnostic system. The diagnostic server 200 calculates an evaluation score for each category based on the evaluation score for each evaluation item specified based on the evaluation criteria, and further calculates an evaluation score for the image quality of the entire image to be diagnosed based on the evaluation score for each category.

[0026] Here, an example of a method for calculating the evaluation score of a category based on the evaluation score of the evaluation item will be described using the example shown in FIG. 5. As an example, the evaluation items "sudden streaks", "periodic streaks", "dirt", and "white spots" included in the category "defect" will be referred to. In the example shown in FIG. 5, the evaluation score of "sudden streaks" is 88 points, the evaluation score of "periodic streaks" is 91 points, the evaluation score of "dirt" is 41 points, and the evaluation score of "white spots" is 87 points. The maximum evaluation score is set to 100 points. First, the evaluation score of each evaluation item is subtracted from the maximum score of 100 points to obtain a value, and the obtained value is multiplied by a weight value previously determined for each evaluation item. For simplicity, the weight value is set to 1.0. In the example shown in FIG. 5, the calculated values ​​of each evaluation item are 12 for "sudden streaks", 9 for "periodic streaks", 59 for "dirt", and 13 for "white spots". The maximum value of these calculated values ​​is subtracted from 100 to obtain the evaluation score of the category including these evaluation items. In the above example, since 59 is the maximum value, the evaluation score for the category "Defect" is 41 (=100-59).

[0027] Similarly, for other categories, evaluation scores are calculated that reflect weighting according to the evaluation scores of each evaluation item. In the example shown in Figure 5, the evaluation score for the category "density" is 84 points, the evaluation score for "intra-screen unevenness" is 82 points, the evaluation score for "defects" is 41 points, and the evaluation score for "noise" is 86 points.

[0028] Further, an evaluation score for the entire image is calculated based on the evaluation score for each category and weighting according to the evaluation score. In the example shown in Fig. 5, the weight value is set to 1.0, and the evaluation score for the image is calculated using the maximum value of the calculated values ​​in a manner similar to the calculation method for calculating the evaluation score for a category from the evaluation scores of the above evaluation items. That is, the calculated value for the category "density" is 16 (=(100-84) x 1.0), the calculated value for "intra-screen unevenness" is 18, the calculated value for "defect" is 59, and the calculated value for "noise" is 14, with the maximum value being 59 for the category "defect", so the evaluation score for the image is 41 (=100-59).

[0029] The weighting value used in the above calculation method may be changed according to the user's designation or the embodiment of the image output. This allows the evaluation score of the image to reflect the user's preferences and the tendency in the actual image output. The details of the method of specifying the weighting value will be described later. Note that the above calculation method is merely one example of a method of specifying the evaluation score of a category based on the evaluation score of an evaluation item and a method of specifying the evaluation score of an image based on the evaluation score of a category, and these calculation methods are not limited to the above method. In addition, in the above example, the evaluation score of a category is specified based on the maximum value of the calculated value of the evaluation item, and the evaluation score of an image is specified based on the maximum value of the calculated value of the category. In contrast to this, weighting may be set according to the order of magnitude of the calculated value or the value itself, and the evaluation scores of multiple evaluation items may be reflected in the evaluation score of the category, and the evaluation scores of multiple categories may be reflected in the evaluation score of the image.

[0030] <Setting weighting for each evaluation item> The weighting can be set in various ways. Here, a method will be described in which a set of weights determined in advance according to the output characteristics of a job executed by the image processing device 100 is selected, and the user changes the weights for each evaluation item individually as necessary. Although the user may set weights for all of the individual evaluation items, using such a set of weights can reduce the effort required for setting weights.

[0031] Fig. 6 is a diagram for explaining a set of weight values ​​determined according to output characteristics, Fig. 6(A) is a diagram showing an example of input information by a user, Fig. 6(B) is a diagram showing an example of a correspondence table between input information and JOB categories, and Fig. 6(C) is a diagram showing an example of a setting table of weight values ​​in JOB categories. The input information in Fig. 6(A) is registered in the diagnostic server 200 by, for example, the user operating the terminal device 300 to input the information. The tables in Fig. 6(B) and Fig. 6(C) are held, for example, in a storage device of the diagnostic server 200 (for example, the auxiliary storage device 203 shown in Fig. 3).

[0032] In the example shown in FIG. 6(A), "industry / business type", "product", "user name", "base", and "machine number" are set as items of input information. "Industry / business type" is information indicating the industry or business type in which the image processing device 100 is used, and in the example shown in FIG. 6(A), printing, architecture, design office, etc. are registered. "Product" is information indicating the type of product of the image output by the image processing device 100, and in the example shown in FIG. 6(A), transaction, magazine, drawing, graphic, etc. are registered. "User name" is information indicating the owner of the image processing device 100, and in the example shown in FIG. 6(A), A printing, B architecture office, C design office, etc. are registered. "Base" is information indicating the location where the image processing device 100 is installed, and in the example shown in FIG. 6(A), S factory, T factory, head office, U factory, etc. are registered. "Machine number" is identification information of each image processing device 100, and S-1, T-1, 1, U-1, etc. are registered.

[0033] The input information by the user includes selection items selected from information prepared in advance and free input items input by the user individually. Selection items are items set in advance by the provider of the diagnostic system, etc., and are input by the user selecting one of the items. Free input items are items input by the user's individual input operation. In the example shown in FIG. 6(A), "industry / business type" and "product" are selection items, and "user name", "location", and "unit number" are free input items. Therefore, in the example shown in FIG. 6(A), "printing-transaction", "printing-magazine", "architecture-drawing", "design office-graphics", etc. are prepared in advance as the selection item "industry / business type-product", and the user selects and registers the item that suits him / her from these items. In addition, the user inputs and registers each information of "user name", "location", and "unit number".

[0034] The selection items of the input information are associated with JOB categories. The JOB category is information that specifies the type of set of weight values. Here, as an example, it is assumed that three types of JOB categories, low image quality, medium image quality, and high image quality, are set. In the correspondence table shown in FIG. 6(B), the selection items "Print-Transaction", "Print-Documents and Graphs", "Design Firm-Transaction", etc. are associated with low image quality, and the selection items "Print-Magazine", "Print-Graphics", "Print-Drawings", "Design Firm-Magazine", etc. are associated with high image quality. When the diagnostic server 200 accepts the selection of a selection item, it refers to the correspondence table as shown in FIG. 6(B) to specify the JOB category that corresponds to the selected selection item.

[0035] In each JOB category, a weight value is set for each evaluation item. In the setting table shown in Fig. 6(C), weight values ​​are set for the evaluation items "density," "in-plane unevenness," "periodic unevenness," "sudden streaks," "periodic streaks," "stains," "white spots," and "roughness" for each of the JOB categories of low, medium, and high image quality.

[0036] Next, the free input items will be described. The information of the free input items entered by the user makes it possible to identify individual image processing devices 100. The diagnostic server 200 stores the image processing devices 100 identified by the information of the selection items and free input items in association with the weighting values ​​for each evaluation item set for the image processing devices 100. This association information is stored, for example, in a storage device of the diagnostic server 200 (for example, the auxiliary storage device 203 shown in FIG. 3).

[0037] FIG. 7 shows an example of weighting values ​​set for individual image processing devices 100, where FIG. 7(A) shows weighting values ​​set based on selected items, and FIG. 7(B) shows a state where weighting values ​​for individual evaluation items have been changed. In the example shown in FIG. 7(A), the identification information of the image processing device 100 is "Printing-Transaction-A Printing-S Factory-S-1" based on the input information of the selected items and the free input items. Since the input information of the selected item is "Printing-Transaction", the JOB category is "Low image quality". And the weighting values ​​for each evaluation item are "Density": 1.0, "Intra-surface unevenness": 0.7, "Cyclic unevenness": 0.7, "Sudden streaks": 0.7, "Cyclic streaks": 0.7, "Stain": 0.6, "Whiteout": 1.0, and "Roughness": 1.0 according to the setting table shown in FIG. 6(C).

[0038] Here, assume that a stricter evaluation is desired for the evaluation item "soiling" for machine S-1 located at the base "S Factory" of user name "A Printing." In this case, the user can change the weight value of the individual evaluation item "soiling" for the weight value set shown in FIG. 7(A). In the example shown in FIG. 7(B), the weight value of the evaluation item "soiling" is changed from 0.6 shown in FIG. 7(A) to 1.3.

[0039] Next, the adjustment of the set of weight values ​​will be described. The change of weight values ​​described with reference to Fig. 7 was performed for each evaluation item individually. On the other hand, there are cases where it is desired to evaluate the image quality of the image strictly overall, or conversely, to evaluate it loosely. In such cases, it is conceivable to adjust the weighting in the target image processing device 100 by changing the weight values ​​of each evaluation item overall within a predetermined range.

[0040] FIG. 8 is a diagram showing an example of a weight adjustment table. FIG. 8 shows an example of an adjusted value (hereinafter, referred to as an "adjusted value") for a low image quality weight set. The weight adjustment is performed within a certain range determined in advance, with the weight set in the setting table shown in FIG. 6(C) as the standard. Here, the weight set can be adjusted at a level of -5 to +5 for the standard weight set. Note that the example shown in FIG. 8 shows weight sets for each evaluation item for levels +5, standard (level 0), and level -5, but for levels between level +5 and the standard and levels between the standard and level -5, a weight set with an adjusted value is set for each level value 1, for example. In the example shown in the figure, when the level is a positive value, the value of each weight is larger than the standard, and when the level is a negative value, the value of each weight is smaller than the standard.

[0041] FIG. 9 is a diagram showing the state in which the weight values ​​for each evaluation item have been adjusted. As an example, consider the case in which an adjustment is made to lower the level of the weight values ​​for machine S-1, which is located at the base "S Factory" of user "A Printing." The standard weight value set for machine S-1 is assumed to be the values ​​shown in FIG. 7(A). Then, assume that this weight value set has been adjusted to level -2. Referring to FIG. 9, the weight values ​​for each evaluation item have been adjusted to values ​​lower than the values ​​shown in FIG. 7(A).

[0042] The method of setting the adjustment value for each level is not particularly limited. For example, a predetermined value for each level may be stored in a storage device of the diagnostic server 200 (for example, the auxiliary storage device 203 shown in FIG. 3), or may be calculated from a standard weight set and the selected level using a predetermined formula. After adjusting the weight set as described with reference to FIGS. 8 and 9, the weights for each evaluation item may be changed individually as described with reference to FIG. 7.

[0043] In this manner, the diagnostic server 200 calculates the evaluation score for each category using the weighting values ​​for each evaluation item set for each user's image processing device 100, and calculates the evaluation score for the image. Therefore, even if images having the same evaluation scores for each evaluation item are output from different image processing devices 100, the evaluation scores for the images will differ depending on the settings of these weighting values.

[0044] FIG. 10 is a diagram showing an example of weighting for each image processing device 100. FIG. 10 shows an example of a weighting set set for each image processing device 100 specified by the input information shown in FIG. 6(A). Referring to FIG. 10, the weighting set for each image processing device 100 differs depending on whether the JOB category is low image quality, medium image quality, or high image quality. In addition, both the T-1 machine of A Printing and the 1st machine of C Design Office are set to high image quality, but the 1st machine of C Design Office is adjusted to a level of +1, so that the weighting value is larger than that of the T-1 machine of A Printing for some evaluation items. In addition, the U-1 machine of U Factory of A Printing has a characteristic weighting set in which the weighting values ​​for the evaluation items other than "dirt" are large, and only the weighting value for "dirt" is very small, because the weighting values ​​for each evaluation item are set manually.

[0045] As an example, consider the case where the same image is printed by machine S-1 located at A Printing's S factory, machine T-1 located at T factory, and machine U-1 located at U factory. The evaluation scores for each evaluation item of the image are as shown in Figure 5. That is, "density" is 84 points, "intra-page unevenness" is 93 points, "periodic unevenness" is 82 points, "sudden streaks" is 88 points, "periodic streaks" is 91 points, "soiling" is 41 points, "white space" is 87 points, and "roughness" is 86 points.

[0046] In this case, when the evaluation score for each category is calculated using the weighting value set shown in Fig. 10 and the calculation method described with reference to Fig. 5, for Machine S-1, the category "density" is 84 points, "intra-surface unevenness" is 87.4 points, "defect" is 64.6 points, and "noise" is 86 points. Similarly, for Machine T-1, the category "density" is 84 points, "intra-surface unevenness" is 82 points, "defect" is 41 points, and "noise" is 86 points. For Machine U-1, the category "density" is 84 points, "intra-surface unevenness" is 78.4 points, "defect" is 82.3 points, and "noise" is 86 points.

[0047] Then, when the evaluation score of the image is calculated based on the evaluation score for each of these categories, it is 64.6 points for S-1, 41 points for T-1, and 78.4 points for U-1. Thus, even for the same image (image with the same evaluation score for each evaluation item), the evaluation value of the obtained image differs based on the difference in the weight value set set for each image processing device 100. Therefore, the diagnosis result of the image based on the evaluation value will be an individual diagnosis result for each image processing device 100, even if the image used for diagnosis is the same.

[0048] Here, the relationship between the weight set shown in FIG. 10 and the evaluation results will be examined in more detail. Referring to the evaluation scores for each evaluation item shown in FIG. 5, the evaluation item "dirt" is the lowest at 41 points. On the other hand, referring to the weight values ​​for the evaluation item "dirt" for each of the above-mentioned S-1, T-1, and U-1 machines in the weight set shown in FIG. 10, S-1 has a small value of 0.6, and U-1 has a small value of 0.3, while T-1 has a large value of 1.0. This means that S-1 and U-1 machines do not place importance on the presence or absence of dirt in the image, while T-1 places importance on the presence or absence of dirt in the image. As a result, the evaluation score of the image calculated using the weighting value set shown in Figure 10 was low (41 points) for machine T-1, which places importance on dirt, while machines S-1 and U-1, which do not place as much importance on dirt as machine T-1, received higher evaluation scores than machine T-1 (64.6 points for machine S-1 and 78.4 points for machine U-1).

[0049] As described above, by setting weight values ​​for each evaluation item individually for each image processing device 100, it is possible to obtain evaluation results that reflect the preferences of the user of the image processing device 100 when evaluating images in the diagnostic server 200.

[0050] <Setting weight values ​​based on history information of image processing device 100> In the above embodiment, the weight value of each evaluation item is set based on the user's input. In contrast, it is conceivable to automatically set the weight value of each evaluation item based on the usage mode of the image processing device 100 by the user. When the image processing device 100 executes the output process of an image, it acquires history information regarding the output. The history information includes, for example, information on the image quality correction mode and the output mode selected at the time of image formation in the image processing device 100. The image quality correction mode is an operation mode in which the image quality of the output image is corrected according to the type of image. In the image quality correction mode, for example, by specifying a photo, graphic, text, etc. as the type of image, correction is performed to change the number of color gradations and contrast of the image to be output. The output mode is an operation mode in which the image quality of the output image is controlled according to the settings of the image processing device 100. In the output mode, for example, by specifying standard, high image quality, high definition, etc. as the type of output, the resolution and the number of color gradations are controlled in the image output operation.

[0051] When the image processing device 100 performs output using these image quality correction modes and output modes, the image quality is changed according to the selected operation mode, so that in the output image, some evaluation items stand out in image diagnosis and some do not. For this reason, when a certain user continuously uses a certain image quality correction mode or output mode, it is assumed that the user has a preference for emphasizing or not emphasizing a certain evaluation item regarding the image quality of the output image. Therefore, it is conceivable to prepare a weight value set in which the weight value of a certain evaluation item is set to a large value or a small value corresponding to these image quality correction modes and output modes, and apply it according to the tendency of the operation mode used in the image processing device 100.

[0052] FIG. 11 is a diagram showing an example of a weighting value set prepared corresponding to an operation mode. In the example shown in FIG. 11, a weighting value set for each evaluation item as a calculation parameter is set corresponding to the settings of an image correction mode and an output mode. In the illustrated example, the types of evaluation items are the same as those shown in FIG. 6(C), FIG. 7, etc. In the example shown in FIG. 11, "standard", "photo (portrait)", "photo (landscape)", "graphic", "office", "text", etc. are registered as image quality correction modes. In addition, "standard", "high image quality", "high definition", etc. are registered as output modes. Then, for images output by combinations of these image quality correction modes and output modes, the following weighting categories are classified: "general", "photo (portrait)", "photo (landscape)", "figure", "table / graph", and "character / thin line". In FIG. 11, the weighting category is information that specifies the type of weighting value set.

[0053] In the example shown in FIG. 11, when the image correction mode is "standard" and the output mode is "standard", the weighting category is "general", and the weight value set has a weight value of 1 for all evaluation items. Based on the weight values ​​in this case, for example, when the image correction mode is "photo (person)" and the output mode is "high image quality", the weight values ​​of the evaluation items "periodic unevenness", "sudden streaks", "periodic streaks", and "roughness" are large, and the weight value of "whiteout" is small. Also, when the image correction mode is "graphic" and the output mode is "high definition", the weight values ​​of the evaluation items "sudden streaks", "periodic streaks", "stains", "whiteout", and "roughness" are small. In this way, individual weight value sets are set according to the image correction mode and the output mode.

[0054] When the image processing device 100 executes an operation such as outputting an image, it acquires history information regarding the executed processing. When the above-mentioned image correction mode or output mode is used, information on the used operation mode is included in the history information. This history information is transmitted from the image processing device 100 to the diagnostic server 200, and the diagnostic server 200 identifies the operation mode used in the image processing device 100 based on the acquired history information, and selects a weighting value set to be applied according to the identified operation mode.

[0055] Here, the diagnostic server 200 may set a change condition for the weight set based on the tendency of the operation mode used in the image processing device 100, and select a weight set to be applied when this change condition is satisfied. For example, the change condition may be that the rate at which a specific operation mode is used is greater than a predetermined threshold in image output by the image processing device 100 for a certain period or a certain number of times, and a weight set prepared to correspond to the specific operation mode may be applied. Also, the change condition may be that a specific operation mode is used continuously in image output by the image processing device 100 for a certain period or a certain number of times, and a weight set prepared to correspond to the specific operation mode may be applied.

[0056] As an example of a method of applying a weighting value set, it is possible to initially apply a weighting value set of the weighting category "general" in FIG. 11 (image correction mode "standard" and output mode "standard"), and then apply a weighting value set according to the tendency of using the operation mode in the image processing device 100. For example, assume that an image processing device 100 frequently outputs images with text drawn on them, satisfying the condition for changing the weighting value set. In this case, the diagnostic server 200 changes the weighting value set to be applied to this image processing device 100 from the weighting value set of the weighting category "general" in FIG. 11 to the weighting value set of the weighting category "characters and thin lines".

[0057] Here, the relationship between the weight value set and the evaluation result shown in FIG. 11 will be described. As an example, consider a case where the same image is output by two image processing devices 100 that are often used in operation modes with different weighting categories. One image processing device 100 (referred to as "Unit A") is often used in an image quality correction mode of "office" and an output mode of "high definition". In the example shown in FIG. 11, the combination of this operation mode is a weighting category of "table / graph". The other image processing device 100 (referred to as "Unit B") is often used in an image quality correction mode of "photo (portrait)" and an output mode of "high image quality". In the example shown in FIG. 11, the combination of this operation mode is a weighting category of "photo (portrait)".

[0058] Consider the case where the same image is output by machine A and machine B. The evaluation scores for each evaluation item of the image are as shown in Figure 5. That is, "density" is 84 points, "intra-screen unevenness" is 93 points, "periodic unevenness" is 82 points, "sudden streaks" is 88 points, "periodic streaks" is 91 points, "dirt" is 41 points, "white space" is 87 points, and "roughness" is 86 points.

[0059] When the change condition of the weight value set is satisfied, the weight value set of the weight value category "Table / Graph" is applied to the machine A. When the change condition of the weight value set is satisfied, the weight value set of the weight value category "Photo (People)" is applied to the machine B. Therefore, when the evaluation score for each category is calculated using the weight value set shown in FIG. 11 and the calculation method described with reference to FIG. 5, the category "Density" is 84 points, "Intra-plane unevenness" is 84.6 points, "Defect" is 58.7 points, and "Noise" is 90.2 points for the machine A. Similarly, the category "Density" is 84 points, "Intra-plane unevenness" is 73.6 points, "Defect" is 41 points, and "Noise" is 83.2 points for the machine B.

[0060] Then, when the evaluation score of the image is calculated based on the evaluation scores for each of these categories, it is 58.7 points for machine A and 41 points for machine B. Thus, even for the same image (images with the same evaluation scores for each evaluation item), the evaluation values ​​obtained for the image differ based on the difference in the weighting value set according to the tendency of the operation mode used in image processing. Therefore, the diagnosis result of the image based on the evaluation value will be an individual diagnosis result for each image processing device 100, even if the image used for diagnosis is the same.

[0061] Although the embodiment of the present invention has been described above, the technical scope of the present invention is not limited to the above embodiment. Various modifications and alternative configurations that do not depart from the scope of the technical idea of ​​the present invention are included in the present invention.

[0062] <Additional Notes> (((1))) An information processing system comprising one or more processors, the processor storing weighting values ​​for each evaluation item related to image quality in a storage device, acquiring image data to be evaluated, evaluating the image data for each evaluation item based on evaluation criteria for each evaluation item, and evaluating the image quality of an image related to the image data based on the evaluation results for each evaluation item and the weighting values ​​for the evaluation items stored in the storage device. (((2))) The information processing system described in (((1))) is characterized in that the storage device holds a table that corresponds the sets of weighting values ​​for a plurality of the evaluation items with the output characteristics of an image, and the processor acquires information that identifies the output characteristics of an equipment that outputs the image data in response to a user's operation, and based on the table, associates the set of weighting values ​​corresponding to the output characteristics of the equipment identified by the information with the equipment, and applies the set of weighting values ​​associated with the equipment to the evaluation results for each evaluation item for the image output by the equipment, thereby evaluating image quality. (((3))) The information processing system described in (((2))) is characterized in that the processor accepts individual changes to weight values ​​for one or more evaluation items in the set of weight values ​​for each device to which the set of weight values ​​is associated in response to user operation, and updates the set of weight values. (((4))) The information processing system described in (((2))) is characterized in that the processor updates the set of weight values ​​by accepting changes to adjust each weight value included in the set of weight values ​​for each device to which the set of weight values ​​is associated within a predetermined range in response to user operation. (((5))) The information processing system of any of (((1))) to (((4))), characterized in that the processor acquires historical information regarding image output from a device that outputs the image data, updates the weighting value applied to the evaluation of the image output by the device based on information regarding image quality contained in the acquired historical information, and applies the updated weighting value to the evaluation result for each evaluation item for the image output by the device to evaluate the image quality. (((6))) The processor, When a setting related to image quality in the device satisfies a predetermined condition in a plurality of image outputs by the device, the weight value is updated based on the history information in the plurality of image outputs. The information processing system according to (((5))), (((7))) A program that causes a computer to realize the following functions: storing weight values ​​for each evaluation item related to image quality in a storage device; acquiring image data to be evaluated; evaluating the image data for each evaluation item based on evaluation criteria for each evaluation item; and evaluating the image quality of an image related to the image data based on the evaluation results for each evaluation item and the weight values ​​for the evaluation items stored in the storage device.

[0063] According to the information processing system of (((1))), it is possible to evaluate image quality in a manner that reflects the user's preferences, as compared to a configuration in which image quality is evaluated based on a uniform standard. According to the information processing system of (((2))), the effort required for setting weight values ​​can be reduced compared to a configuration in which the user inputs the weight values ​​of each evaluation item individually. According to the information processing system of (((3))), in comparison with a configuration in which image quality is evaluated based on weighting values ​​set according to output characteristics, it is possible to perform evaluation according to individual settings for each device. According to the information processing system of (((4))), in comparison with a configuration in which image quality is evaluated based on weighting values ​​set according to output characteristics, it is possible to perform evaluation according to the settings for each device. According to the information processing system of (((5))), the effort required for setting weight values ​​can be reduced compared to a configuration in which a user inputs weight values ​​for each evaluation item according to the use of the equipment. According to the diagnostic system of (((6))), unlike a configuration in which weight values ​​are updated based on a single image output, it is possible to reflect the user's preferences regarding image quality. According to the program of (((7))), in a computer having this program installed, it is possible to evaluate image quality in a manner that reflects the user's preferences, as compared to a configuration in which image quality is evaluated based on uniform standards. [Explanation of symbols]

[0064] 100...image processing device, 110...image forming unit, 120...image reading unit, 130...display device, 140...operation device, 150...communication interface, 160...storage device, 170...control device, 200...diagnosis server, 201...processor, 202...main storage device, 203...auxiliary storage device, 204...communication interface, 300...terminal device, 301...processor, 302...main storage device, 303...auxiliary storage device, 304...display device, 305...input device, 306...communication interface

Claims

1. One or more processors; The processor, A weight value for each evaluation item related to image quality is stored in a storage device; Acquire image data to be evaluated; The image data is evaluated for each evaluation item based on an evaluation criterion for each evaluation item; evaluating the image quality of the image related to the image data based on an evaluation result in the evaluation for each of the evaluation items and a weighting value for the evaluation item stored in the storage device; An information processing system comprising:

2. the storage device holds a table in which the sets of weight values ​​for the plurality of evaluation items correspond to output characteristics of an image; The processor, Acquire information specifying the output characteristics of a device that outputs the image data in response to a user operation; based on the table, associating a set of weight values ​​corresponding to output characteristics of a device identified by the information; With respect to the image output by the device, applying the set of weight values ​​associated with the device to the evaluation results for each of the evaluation items to evaluate the image quality.

2. The information processing system according to claim 1 .

3. The processor, accepting individual changes to weights for one or more evaluation items in the set of weights for each device associated with the set of weights in response to a user operation, and updating the set of weights; 3. The information processing system according to claim 2 .

4. The processor, accepting a change to adjust each weight value included in the set of weight values ​​in each device associated with the set of weight values ​​within a predetermined range in response to a user operation, and updating the set of weight values; 3. The information processing system according to claim 2 .

5. The processor, acquiring history information relating to image output from a device that outputs the image data; updating the weighting value applied to the evaluation of the image output by the device based on information regarding image quality included in the acquired history information; applying the updated weighting value to the evaluation result for each evaluation item for the image output by the device to evaluate the image quality.

2. The information processing system according to claim 1 .

6. The processor, When a setting related to image quality in the device satisfies a predetermined condition in a plurality of image outputs by the device, the weight value is updated based on the history information in the plurality of image outputs.

6. The information processing system according to claim 5 .

7. On the computer, A function of storing weight values ​​for each evaluation item related to image quality in a storage device; A function for acquiring image data to be evaluated; a function of evaluating the image data for each evaluation item based on evaluation criteria for each evaluation item; a function of evaluating the image quality of the image related to the image data based on an evaluation result in the evaluation for each of the evaluation items and a weight value for the evaluation item stored in the storage device; A program for realizing the above.

Images